A behavioral modeling approach to prevent unauthorized large-scale documents copying from digital libraries

Abstract

There are many issues concerning information security of digital libraries. Apart from traditional information security problems there are some specific ones for digital libraries. In this work we consider a behavioral modeling approach to discover unauthorized copying of a large amount of documents from a digital library. Supposing the regular user has interest in semantically related documents, we treat referencing to semantically unrelated documents as anomalous behavior that may indicate attempt of unauthorized large-scale copying. We use an adapted anomaly detection approach to discover attempts of unauthorized large-scale documents copying. We propose a method for constructing classifiers and profiles of regular users' behavior based on application of Markov chains. We also present the results of experiments conducted within development of a prototype digital library protection system. Finally, examples of a normal profile and an automatically detected anomalous session derived from the real data logs of a digital library illustrate the suggested approach to the problem.